Backed by Y Combinator.
About the role
We’re looking for a Research Manager to lead research that makes HUD’s agent training data and evals more useful for improving frontier models. You’ll lead research engineers through ambiguous projects, and turn findings into methods that can be used at scale. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Experience leading technical research projects to completion, from an open question to evidence, a decision, and a working result
- Experience directly managing and mentoring researchers or research engineers while remaining engaged in technical work
- A strong understanding of machine learning and reinforcement learning, including how training objectives, data, and feedback shape model behavior
- Experience with agent training data, evals, benchmarks, synthetic data, or model evaluation infrastructure
- Sound experimental judgment: you can distinguish a useful training signal from a task or metric that only looks convincing
- Strong written communication and the ability to explain methods and findings to researchers, engineers, and external partners
More about this role
HUD 's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.
We’re looking for a Research Manager to lead research that makes HUD’s agent training data and evals more useful for improving frontier models. You’ll lead research engineers through ambiguous projects, and turn findings into methods that can be used at scale.
You’ll stay close to the technical work while helping the team choose the right questions, run rigorous experiments, and deliver results. This role calls for an understanding of how and why models train: which signals teach useful behavior, where apparent progress is misleading, and how data and eval design can change outcomes.
Set the research direction for data quality, including how HUD measures how training data impacts agent behavior and performance after training, and what makes...
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